This paper proposes a structure exploiting algorithm for solving non-convex power system state estimation problems in distributed fashion. Because the power flow equations in large electrical grid networks are non-convex equality constraints, we develop a tailored state estimator based on Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) method, which can handle the nonlinearities efficiently. Here, our focus is on using Gauss-Newton Hessian approximations within ALADIN in order to arrive at at an efficient (computationally and communicationally) variant of ALADIN for network maximum likelihood estimation problems. Analyzing the IEEE 30-Bus system we illustrate how the proposed algorithm can be used to solve highly non-trivial network state estimation problems. We also compare the method with existing distributed parameter estimation codes in order to illustrate its performance.
@article{arxiv.1903.08956,
title = {Distributed State Estimation for AC Power Systems using Gauss-Newton ALADIN},
author = {Xu Du and Alexander Engelmann and Yuning Jiang and Timm Faulwasser and Boris Houska},
journal= {arXiv preprint arXiv:1903.08956},
year = {2019}
}